The augmentation to marginalize N out as a latent discrete requires setting an upper bound, K, to loop through. If K is too small the posterior will be truncated. Need to check for every N.
## null device
## 1
Effective samples sizes using rstan::monitor following Hoffman and Gelman (2014) to be more reliable and accurate (as in Monnahan et al. 2017) - UPDATE - now follow Vehtari et al. 2019.
effective sample sizes (should be > 100) - Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2019). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
## [1] 1.003609
## [1] 1675
## [1] 2564
Examine posterior prredictions of total counts across all 5 visits
RMSE of posterior predictive
## [1] 5.070722
Posterior predictive check for each visit
RMSE of posterior predictive for observations per visit
## [1] 2.956443
RMSE of posterior predictive
## [1] 1.322162
The augmentation to marginalize N out as a latent discrete requires setting an upper bound, K, to loop through. If K is too small the posterior will be truncated. Need to check for every N.
## null device
## 1
Effective samples sizes using rstan::monitor following Hoffman and Gelman (2014) to be more reliable and accurate (as in Monnahan et al. 2017) - UPDATE - now follow Vehtari et al. 2019.
effective sample sizes (should be > 100) - Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2019). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
## [1] 1.00399
## [1] 2346
## [1] 2987
Examine posterior prredictions of total counts across all 5 visits
RMSE of posterior predictive
## [1] 1.315241
Posterior predictive check for each visit
RMSE of posterior predictive for observations per visit
## [1] 0.7504749
RMSE of posterior predictive
## [1] 0.3356226
The augmentation to marginalize N out as a latent discrete requires setting an upper bound, K, to loop through. If K is too small the posterior will be truncated. Need to check for every N.
## null device
## 1
Effective samples sizes using rstan::monitor following Hoffman and Gelman (2014) to be more reliable and accurate (as in Monnahan et al. 2017) - UPDATE - now follow Vehtari et al. 2019.
effective sample sizes (should be > 100) - Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2019). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. arXiv preprint arXiv:1903.08008.
## [1] 1.003496
## [1] 2495
## [1] 3415
Examine posterior prredictions of total counts across all 5 visits
RMSE of posterior predictive
## [1] 1.404227
Posterior predictive check for each visit
RMSE of posterior predictive for observations per visit
## [1] 0.8349771
RMSE of posterior predictive
## [1] 0.3734131